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Chapter 1

Chapter 1. Data Structures and Algorithms. Primary Goals. Present commonly used data structures Introduce the idea of tradeoffs; reinforce the concept of costs and benefits associated with every data structure Measure the effectiveness of a data structure or algorithm.

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Chapter 1

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  1. Chapter 1 Data Structures and Algorithms

  2. Primary Goals • Present commonly used data structures • Introduce the idea of tradeoffs; reinforce the concept of costs and benefits associated with every data structure • Measure the effectiveness of a data structure or algorithm

  3. Computer Programming Goals • There are two, sometime conflicting: • To design an algorithm that is easy to understand, code and debug • To design an algorithm that make efficient use of the computer’s resources. • This course is mainly concerned with goal number 2.

  4. What is a data structure? • In the most general, a data structure is any data representation and its associated operations. • More typically, a data structure is meant to be an organization or structuring for a collection of data items.

  5. Efficiency and Cost • A solution to a problem is efficient if it solves the problem within the required resource constraints • Examples: total space to store data, time allowed to perform • The cost of a solution is the amount of resources that the solution consumes. • Often the cost is measured in terms of one key resource

  6. Efficiency and Cost • A solution can also be efficient if it requires fewer resources than other know solutions • When solving problems, you will want to analyze the different data structures available and choose accordingly. • You want to make sure you choose both efficient and cost effective solutions • Conversely, you don’t need a sledge hammer to put in a thumbtack

  7. Choosing your Data Structure • You should follow these steps • Analyze your problem to determine the resource constraints that any solution must meet • Determine the basic operations that must be supported and quantify the resource constraints for each operation • Select the data structure that best meets these requirements

  8. Operational Questions • When choosing data structures, here are some questions to ask. • Are all data items inserted into the data structure at the beginning, or are the insertions interspersed with other operations? • Can data items be deleted? • Are all data items process in some well-defined order, or is search for specific data items allowed?

  9. Abstract Data Types and Data Structures • Type a collection of values • A simple type has no subparts • An aggregate type or composite type contains several pieces of information • A data item is a piece of information or a record whose value is drawn from a type. • It is said to be a member of a type • A data type is a type together with a collection of operations to manipulate the type.

  10. Abstract Data Types and Data Structures • There is a distinction between the logical concept of a data type and its physical implementation • Example: Trees can be implemented both in a linked fashion or an array based fashion • An Abstract Data Type (ADT) is the realization of a data type as a software component. • An ADT does not specify how the data type is implemented

  11. Abstract Data Types and Data Structures • A data structure is the implementation for an ADT • ADT’s allow programmers to manage complexity through abstraction • Data items have both a logical and a physical form • The definition of the data item in terms of an ADT is the logical form • The implementation of the data item within the data structure is its physical form

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